In federated learning of supervised models, typically it is assumed that users have access to pairs of inputs and outputs. In most cases, for any given input, the output is naturally derived from user interactions or can be easily obtained. For example, in the next-word prediction task, the output is simply the next word typed by the user [1]}.
In distributed training of UA models, however, the embeddings are not pre-defined.
Moreover, even when users know their own embeddings, they need to have access to the embeddings of other users, so that the model can be trained to assign predicted embeddings to be not only close to the reference one, but also far away from other embeddings.
